POS-tagging to highlight the skeletal structure of sentences

TL;DR

This paper introduces a transfer learning-based POS tagging model using BERT to identify sentence structures, specifically fine-tuned on Russian text, with potential to improve NLP tasks like machine translation.

Contribution

It presents a novel application of BERT for POS tagging in Russian, focusing on extracting sentence skeletal structures, which was not previously explored in this context.

Findings

Effective POS tagging on Russian text

Potential to enhance machine translation tasks

Demonstrated transfer learning benefits

Abstract

This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification. The model, fine-tuned on Russian text, demonstrating its effectiveness. The approach offers potential applications in enhancing natural language processing tasks, such as improving machine translation. Keywords: part of speech tagging, morphological analysis, natural language processing, BERT.